In Sections REF  and REF , all the HaloNet-50 models use the same layer allocations and channels widths as the standard ResNet-50 [1]} model. Both ResNet-50 and HaloNet-50 models were trained for 150 epochs on \(256\times 256\)  size images with a learning rate of \(0.1\) . For the experiments with RandAugment, we used a weight decay of \(0.00004\)  for the settings that used RandAugment [2]}, and \(0.00008\)  otherwise. Using a weight decay of \(0.00008\)  with RandAugment seemed to have a negative impact on accuracies with ResNet-50. We used a RandAugment magnitude of 10 in these sections. For HaloNet-50, we used a block size \(b=8\) , and halo \(h=3\) . We fixed the number of channels per head to be 16. For the SASA models in section, we used a pixel centered window of size \(7\times 7\)  following [3]}.
